Summary of Synthetic Data Generation with Llm For Improved Depression Prediction, by Andrea Kang et al.
Synthetic Data Generation with LLM for Improved Depression Prediction
by Andrea Kang, Jun Yu Chen, Zoe Lee-Youngzie, Shuhao Fu
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed pipeline utilizes Large Language Models (LLMs) to generate synthetic data for improving depression prediction models. Starting from unstructured text data from clinical interviews, the pipeline generates synthetic data through chain-of-thought prompting. This involves two key steps: generating a synopsis and sentiment analysis based on the original transcript and depression score, followed by generating a synthetic synopsis/sentiment analysis based on summaries generated in the first step and a new depression score. The synthetic data is satisfactory in terms of fidelity and privacy-preserving metrics, balancing the distribution of severity in the training dataset. This approach significantly enhances the model’s capability to predict the intensity of patients’ depression. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to create fake data that can help doctors better detect depression using artificial intelligence. They used large language models to generate text based on real conversations between therapists and patients, creating synthetic data that mimics the original conversations. This approach helps solve two big problems: it ensures patient privacy while also addressing the lack of data available for training depression detection models. The fake data was so realistic that it even helped improve the accuracy of the model’s predictions about how severe a patient’s depression is. |
Keywords
» Artificial intelligence » Prompting » Synthetic data
